This proposal seeks continuation of a multi-disciplinary research effort at F.A..U.~s Center for Complex Systems between neuroscience, psychology and physics that seeks to uncover the basic organizational principles and mechanisms underlying higher brain function. In particular, questions asked are how human beings coordinate their actions with environment, how they perceive and categorize the world around them, and how they learn new skills. The principal hypothesis guiding the research, which embraces theory, computation and experiment, is that brain and behavior are self-organized, governed by coupled dynamical rules on several scales of observation. Such dynamics determine the collective activity of the nervous system, generating the time course of coordination states on a given level of description. Three major experimental sections are proposed to test predicted features of specific dynamical models in the context of the theory of self- organization. In each case, detailed hypotheses are made regarding the neural structures involved and their selective, task-specific engagement over time (to be obtained through a synergism of high density electrode, SquID arrays and fMRI). This information in turn will be used for two purposes: first, to meet the much needed demand for image analysis, compression and visualization that preserves the real-time dynamics of the brain; and, second, to develop theoretical models of global brain function that are based on realistic neuroanatomical and neurophysiological considerations. In addition, a number of new paradigms are proposed aimed at exposing novel dynamical processes that will promote further theoretical developments. Identifying key quantities for collective activity at both brain and behavioral levels and the dynamical rules by which self-organization occurs, should put us in a much better position to understand functional brain disorders. Since the relevant information will be known at the macroscopic level, this knowledge will place constraints on which microscopic processes and variables are relevant and which are not.